Abstract
Communication technologies are evolving drastically in recent years. However, the scarcity of spectrum began to appear with the accelerating usage of various communication technologies, as well as the preservation of traditional channel access methods. Cognitive Radio (CR) is an innovative solution for spectrum scarcity. Spectrum sensing is a key task of the CR life-cycle that gains significance as spectrum holes can be detected during this task. This paper studies and compares the performance of the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KMeans-based spectrum sensing technique</b> with the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-cooperative spectrum sensing technique</b> , the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">And-based spectrum sensing technique</b> , and the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Or-based spectrum sensing technique in stationary and mobile CR networks (CRNs).</b> The effect of the fading channel type has also been considered. Small-scale CRNs were simulated using the third version of the network simulator. In this context, two models have been developed. The first was built based on the well-known <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa -\mu $ </tex-math></inline-formula> general fading model to simulate the fading effects. The latter is the noise model to simulate different noise conditions. The results reveal that spectrum sensing techniques provide better performance in stationary networks as compared to mobile networks. Further, our experimental results show that at least three secondary users and about 1500 samples are needed to reach acceptable performance. In addition, the results show that the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KMeans-based technique</b> slightly outperforms the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Or-based technique</b> , especially in highly noisy environments and under severe fading channels.
Highlights
C OGNITIVE Radio (CR) is an intelligence system able to switch between radio access methods as well as transmitting in different portions of the radio spectrum, [1]
There are a plethora of works of spectrum sensing techniques on cognitive radio networks (CRN)
This is because when a secondary users (SUs) wrongly detects the presence of the primary user (PU), it affects the global decision made by all SUs
Summary
C OGNITIVE Radio (CR) is an intelligence system able to switch between radio access methods as well as transmitting in different portions of the radio spectrum, [1]. The reconfigurability of the CR passes through cognition tasks which are: sensing the spectrum, analyzing the spectrum, and making joint decisions on spectrum selections. There are a plethora of works of spectrum sensing techniques on cognitive radio networks (CRN). Most of these types are classified as: energy detection-based, cyclostationary matrix-based, and covariance-based techniques. Machine learning-based techniques are another modern type of innovative spectrum sensing technique [2]. In such methods, the sensing process in detecting the primary user’s activities passes through two phases which are: the feature extraction phase and the decision-making phase
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